Liquefaction hazard analysis is crucial in earthquake-prone regions as it magnifies structural damage. In this study, standard penetration test (SPT) and shear wave velocity (Vs) data of Chittagong City have been used to assess the liquefaction resistance of soils using artificial neural network (ANN). For a scenario of 7.5 magnitude (Mw) earthquake in Chittagong City, estimating the liquefaction-resistance involves utilizing peak horizontal ground acceleration (PGA) values of 0.15 and 0.28 g. Then, liquefaction potential index (LPI) is determined to assess the severity of liquefaction. In most boreholes, the LPI values are generally higher, with slightly elevated values in SPT data compared to Vs data. The current study suggests that the Valley Alluvium, Beach and Dune Sand may experience extreme liquefaction with LPI values ranges from 9.55 to 55.03 and 0 to 37.17 for SPT and Vs respectively, under a PGA of 0.15 g. Furthermore, LPI values ranges from 25.55 to 71.45 and 9.55 to 54.39 for SPT and Vs correspondingly. The liquefaction hazard map can be utilized to protect public safety, infrastructure, and to create a more resilient Chittagong City.

Evaluating petroleum contamination risk and implementing remedial measures in agricultural soil rely on indicators such as soil metal(loid)s and microbiome alterations. However, the response of these indicators to petroleum contamination remains under-investigated. The present study investigated the soil physicochemical features, metal(loid)s, microbial communities and networks, and phospholipid fatty acids (PLFAs) community structures in soil samples collected from long-(LC) and short-term (SC) petroleum-contaminated oil fields. The results showed that petroleum contamination increased the levels of soil total petroleum hydrocarbon, carbon, nitrogen, sulfur, phosphorus, calcium, copper, manganese, lead, and zinc, and decreased soil pH, microbial biomass, bacterial and fungal diversity. Petroleum led to a rise in the abundances of soil Proteobacteria, Ascomycota, Oleibacter, and Fusarium. Network analyses showed that the number of network links (Control vs. SC, LC = 1181 vs. 700, 1021), nodes (Control vs. SC, LC = 90 vs. 71, 83) and average degree (Control vs. SC, LC = 26.244 vs. 19.718, 24.602) recovered as the duration of contamination increased. Petroleum contamination also reduced the concentration of soil PLFAs, especially bacterial. These results demonstrate that brief exposure to high levels of petroleum contamination alters the physicochemical characteristics of the soil as well as the composition of soil metal(loid)s and microorganisms, leading to a less diverse soil microbial network that is more susceptible to damage. Future research should focus on the culturable microbiome of soil under petroleum contamination to provide a theoretical basis for further remediation. (c) 2025 The Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences. Published by Elsevier B.V.

Abandoned farmlands are increasing due to socio-economic changes and land marginalization, and they require sustainable land management practices. Biocrusts are a common cover on the topsoil of abandoned farmlands and play an important role in improving soil stability and erosion resistance. The critical functions of biocrusts are known to mostly rely on their biofilaments and extracellular polymeric substances (EPS), but how these components act at microscopic scale is still unknown, while rheological methods are able to provide new insights into biocrust microstructural stability at particle scale. Here, bare soil and two representative types of biocrusts (cyanobacterial and moss crusts) developed on sandy (Ustipsamments) and sandy loam (Haplustepts) soils in abandoned farmlands in the northern Chinese Loess Plateau were collected at a sampling depth of 2 cm. Changes in the rheological properties of the biocrusts were analyzed with respect to their biofilament network and EPS contents to provide possible explanations. The rheological results showed that compared with bare soil, storage and loss moduli were decreased by the biocrusts on sandy soil, but they were increased by the biocrusts on sandy loam soil. Other rheological parameters tau max, gamma L, gamma YP, and Iz of biocrusts on both soils were significantly higher than those of bare soil, showing higher viscoelasticity. And the moss crusts had about 10 times higher rheological property values than the cyanobacterial crusts. Analysis from SEM images showed that the moss crusts had higher biofilament network parameters than the cyanobacterial crusts, including nodes, crosslink density, branches, branching ratio and mesh index, and biofilament density, indicating that the biofilament network structure in the moss crusts was more compact and complex in contrast to the cyanobacterial crusts. Additionally, EPS content of the moss crusts was higher than that of the cyanobacterial crusts on both soils. Overall, the crosslink density, biofilament density, and EPS content of the biocrusts were significantly and positively correlated with their gamma YP and Iz. The interaction between crosslink density and biofilament density contributed 73.2 % of gamma YP, and that between crosslink density and EPS content contributed 84.0 % of Iz. Our findings highlight the biocrusts-induced changes of abandoned farmland soil rheological properties in drylands, and the importance of biocrust biofilament network and EPS in maintaining abandoned farmland soil microstructural stability to resist soil water/wind erosion and degradation, providing a new perspective for sustainable management of abandoned farmlands.

Buried pipelines are essential for the safe and efficient transportation of energy products such as oil, gas, and various chemical fluids. However, these pipelines are highly vulnerable to ground movements caused by geohazards such as seismic faults, landslide, liquefaction-induced lateral spreading, and soil creep, which can result in potential pipeline failures such as leaks or explosions. Response prediction of buried pipelines under such movements is critical for ensuring structural integrity, mitigating environmental risks, and avoiding costly disruptions. As such, this study adopts a Physics-Informed Neural Networks (PINNs) approach, integrated with a transfer learning technique, to predict structural response (e.g., strain) of both unreinforced and reinforced steel pipes subjected to Permanent Ground Displacement (PGD). The PINN method offers a meshless, simulation-free alternative to traditional numerical methods such as Finite Element Method (FEM) and Finite Difference Method (FDM), while eliminating the need for training data, unlike conventional machine learning approaches. The analyses can provide useful information for in-service pipe integrity assessment and reinforcement, if needed. The accuracy of the predicted results is verified against Finite Element (FE) and Finite Difference (FD) methods, showcasing the capability of PINNs in accurately predicting displacement and strain fields in pipelines under geohazard-induced ground movement.

The ability to predict the soil mechanical parameters swiftly is critical for off-road vehicle mobility. This paper introduces a novel interpretation methodology for determining critical soil mechanical parameters by impact penetration tests, enabling rapid and remote assessment of terramechanics properties. Initially, the method employs the Mohr-Coulomb constitutive model and the Coupled Eulerian-Lagrangian (CEL) finite element method to generate a dataset of soil impact penetration resistance and acceleration responses. Subsequently, a Radial Basis Function (RBF) neural network is employed as a surrogate model and integrated with the Nondominated Sorting Genetic Algorithm II (NSGA-II) to accurately interpret parameters such as density, cohesion, internal friction angle, elastic modulus, and Poisson's ratio. Experimental validation using sand and silty clay from Yangbaijing, Tibet, confirmed the accuracy and robustness of the method. The results indicate that the mean absolute percentage error for interpreted values was below 25%, with relative errors for some key parameters even below 10%. Furthermore, each single-condition calculation was completed on a standard computer in less than one minute. Comparative analyses with other algorithms, including MIGA and POS, demonstrated the superior performance of NSGA-II in avoiding local optima. The proposed interpretation framework offers a rapid, reliable, and remote solution for identifying the soil mechanical properties. Its potential applications range from disaster mitigation and emergency response operations to extraterrestrial soil exploration and other scenarios where in-situ investigations are challenging.

The hilly and mountainous regions of China are characterized by unique features such as small plots of land, steep slopes, fragmented fields, and high soil viscosity, which result in a decline in the efficiency of conventional agricultural machinery, or even render its use impractical. To address this issue, this study developed a micro universal chassis adapted to hilly terrains. First, a four-wheel-drive multifunctional electric micro chassis was designed, considering the terrain characteristics of hilly regions and the agronomic requirements of maizesoybean strip intercropping. Second, the kinematics of the chassis were modeled and analyzed to determine optimal posture control strategies, and a fuzzy RBF neural network-based PID control algorithm was designed to enable dynamic adjustment of the chassis. Then, extensive testing was conducted on the prototype chassis, including straight-line driving tests, steering tests, climbing tests, and passability tests, which demonstrated its excellent operational performance. The straight-line driving tests showed an average lateral deviation of 30 mm and a maximum deviation of 60 mm, while the in-situ steering tests recorded a deviation of 20 mm. Finally, the prototype was applied to field weeding operations, where results indicated that its performance, including travel speed, weeding efficiency, and seedling damage rate, significantly outperformed existing traditional models. The findings suggest that the designed multifunctional micro universal chassis is highly effective for use in hilly and mountainous regions, with superior performance particularly under intercropping systems.

The macroscopic mechanical properties of granular systems largely depend on the complex mechanical responses of force chains at the mesoscopic level. This study offers an alternative to rapidly identify and predict force chain distributions under different stress states. 100 sets of gradation curves that effectively represent four typical continuous gradation distributions are constructed. Numerical specimens corresponding to these gradation curves are generated using the discrete element method (DEM), and a dataset for deep neural network training is established via biaxial compression numerical simulations. The relationship between particle distribution characteristics and force chain structure is captured by the Pix2Pix conditional generative adversarial network (cGAN). The effectiveness of the generated force chain images in reproducing both particle gradation and spatial distribution characteristics is verified through the extraction and analysis of pixel probability distributions across different color channels, along with the computation of texture feature metrics. In addition, a GoogLeNet-based prediction model is constructed to demonstrate the accuracy with which the generated force chain images characterize the macroscopic mechanical properties of granular assemblies. The results indicate that the Pix2Pix network effectively predicts and identifies force chain distributions at peak stress for different gradation

The main problem in expansive soil treatment with steel slag (SS) is the relatively slow hydration reaction that occurs during the initial period. To circumvent this, SS-treated expansive soil activated by metakaolin (MK) under an alkaline environment was investigated in this study. Based on a series of tests on the engineering properties of the treated soil, it can be reported that SS could enhance the strength and compressibility of expansive soil, with strength increasing by approximately 108 % for SS contents exceeding 10 % compared to 3 % lime-treated soil, and the compression index reducing by 20 %. Further addition of MK plays a dual role, enhancing strength for higher SS content while excessive MK leads to strength reduction due to insufficient pozzolanic reactions and hydration product transformation. Expansive and shrinkage behaviors are notably improved, with a 5 % increase in SS content reducing the free swelling ratio by 0.66 %-5.9 %, and the combination of 15 % SS and 6 % MK achieving a nearly 300 % reduction in the linear shrinkage ratio. Microstructural analysis confirms the formation of hydration gels, densification of the soil structure, and reduced macropores, validating the enhanced mechanical and shrinkage resistance properties of the SS-MK-treated expansive soil. Additionally, to develop predictive models for mechanical and the content of hardening agents (SS and MK), the experimental data are processed utilizing a backpropagation neural network (BPNN). The results of BPNN modeling predict the mechanical properties perfectly, and the correlation coefficient (R) approaches up to 0.98.

A series of large-scale shaking table tests was conducted on a pile network composite-reinforced high-speed railway subgrade. The displacement, peak acceleration amplification factor, dynamic soil pressure, and geogrid strain data were used to investigate the dynamic characteristics. The Hilbert-Huang transform spectrum, marginal spectrum, and damping ratios were used to study the seismic energy dissipation characteristics and damage evolution mechanisms of the reinforced subgrade. The results indicate that the graded loading of seismic waves induces a global settlement phenomenon within the subgrade, the displacement phenomenon of the slope is more evident, and the reinforcement effectively mitigates the amplification effect of the peak acceleration along the elevation. The peak and cumulative residual dynamic soil pressures were most significant near the bedding layer, and the upper and middle parts of the subgrade exhibited superior stabilization performance. The geogrid reduced the local vibration variability and enhanced the overall stability. The damage evolution in the middle part of the subgrade was relatively gentle, whereas the slope exhibited a multistage development trend. The internal damage of the subgrade grows slowly at 0.1-0.2 g, faster at 0.2-0.6 g, and rapidly at 0.6-1.0 g.

Soil desiccation cracks and crack networks significantly influence the mechanical properties of soils. Accurate modeling and prediction of crack development are essential for both laboratory research and practical applications in geotechnical engineering and environmental science. In this study, a Desiccation Crack Simulation Program (DCSP) was developed on the MATLAB platform to simulate the evolution of soil desiccation cracks. Based on comprehensive statistical analyses of crack network images from previous studies and detailed observations of crack propagation, we propose a stochastic crack network generation model informed by geometric parameters and crack development processes. The model encompasses five key steps: (1) selection of crack initiation points, (2) crack propagation and intersection, (3) termination of crack growth, (4) secondary crack generation, and (5) final network formation. Key parameters include crack step size, randomized propagation direction, number of initial development points, and crack attraction distance. The DCSP enables both the rapid generation of random crack networks and the prediction of partially developed networks. The program was validated using two soil types, Xiashu soil and Pukou soil, demonstrating its effectiveness in simulating crack evolution. Prediction accuracy improves as crack network develops, highlighting the model's potential for predicting soil desiccation crack patterns.